Alfred Kobsa
Bart P. Knijnenburg
ABSTRACT
Many users welcome personalized services, but are reluctant to provide the information about themselves that personalization requires. Performing personalization exclusively at the client side (e.g., on one's smartphone) may conceptually increase privacy, because no data is sent to a remote provider. But does client-side personalization (CSP) also increase users' perception of privacy?
We developed a causal model of privacy attitudes and behavior in personalization, and validated it in an experiment that contrasted CSP with personalization at three remote providers: Amazon, a fictitious company, and the "Cloud". Participants gave roughly the same amount of personal data and tracking permissions in all four conditions. A structural equation modeling analysis reveals the reasons: CSP raises the fewest privacy concerns, but does not lead in terms of perceived protection nor in resulting self-anticipated satisfaction and thus privacy-related behavior. Encouragingly, we found that adding certain security features to CSP is likely to raise its perceived protection significantly. Our model predicts that CSP will then also sharply improve on all other privacy measures.
- Ahmed, R. and Ho, S. Y. Privacy Concerns of Users for Location-Based Mobile Personalization. CONF-IRM 2011 Proceedings, (2011), Paper 10.Google Scholar
- Ankolekar, A. and Vrandecié, D. Kalpana - enabling client-side web personalization. 17th ACM conference on hypertext and hypermedia, ACM (2008), 21--26. Google ScholarDigital Library
- Awad, N.F. and Krishnan, M.S. The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to be Profiled Online for Personalization. MISQ 30, 1 (2006), 13--28. Google ScholarDigital Library
- Baron, R. M. and Kenny, D. A. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychology 51, 6 (1986), 1173--1182.Google ScholarCross Ref
- Casaló, L. V., Flavián, C., and Guinalíu, M. The role of security, privacy, usability and reputation in the development of online banking. Online Information Review 31, 5 (2007), 583--603.Google ScholarCross Ref
- Cassel, L. N. and Wolz, U. Client Side Personalization. DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries, (2001), 8--12.Google Scholar
- Chellappa, R. K. and Sin, R. Personalization versus Privacy: An Empirical Examination of the Online Consumer's Dilemma. Information Technology and Management 6, 2-3 (2005), 181--202. Google ScholarDigital Library
- Chellappa, R. K. Consumers' Trust in Electronic Commerce Transactions: The Role of Perceived Privacy and Perceived Security. Emory Univ., Atlanta, GA, 2008.Google Scholar
- Chin, E., Felt, A. P., Sekar, V., Wagner, D. Measuring user confidence in smartphone security and privacy. 8th Symp. on Usable Privacy and Security, (2012), 1:1--16. Google ScholarDigital Library
- Coroama, V. and Langheinrich, M. Personalized Vehicle Insurance Rates: A Case for Client-Side Personalization in Ubiquitous Computing. PEP06, CHI Workshop on Privacy-Enhanced Personalization, (2006), 56--59.Google Scholar
- Coroama, V. The Smart Tachograph: Individual Accounting of Traffic Costs and Its Implications. In K.P. Fishkin, B. Schiele, P. Nixon, A. Quigley, eds., Pervasive Computing: 4th Int'l Conf. Springer 2006, 135--152. Google ScholarDigital Library
- Davidson, D. and Livshits, B. MoRePriv: Mobile OSWide Application Personalization. Microsoft Research, Redmond, WA, 2012.Google Scholar
- Fredrikson, M. and Livshits, B. RePriv: Re-imagining Content Personalization and In-browser Privacy. IEEE Symposium on Security and Privacy, (2011), 131--146. Google ScholarDigital Library
- Van de Garde-Perik, E., Markopoulos, P., de Ruyter, B., Eggen, B., and Ijsselsteijn, W. Investigating Privacy Attitudes and Behavior in Relation to Personalization. Social Science Computer Review 26, 1 (2008), 20--43. Google ScholarDigital Library
- Gerber, S., Fry, M., Kay, J., Kummerfeld, B., Pink, G., Wasinger, R. PersonisJ: Mobile, Client-Side User Modelling. In P. Bra, A. Kobsa, D. Chin, eds User Modeling, Adaptation and Personalization. Springer 2010, 111--22. Google ScholarDigital Library
- Guha, S., Cheng, B., and Francis, P. Privad: practical privacy in online advertising. 2011 USENIX conf. on networked systems design and implementation, paper 13. Google ScholarDigital Library
- Ion, I., Sachdeva, N., Kumaraguru, P., and Čapkun, S. Home is safer than the cloud!: privacy concerns for consumer cloud storage. 7th Symposium on Usable Privacy and Security, ACM (2011), 13:1--13:20. Google ScholarDigital Library
- Juels, A. Targeted Advertising... and Privacy Too. In D. Naccache, ed., Topics in Cryptology - CT-RSA 2001. Springer, Berlin/Heidelberg, 2001, 408--424. Google ScholarDigital Library
- Kanich, C., Checkoway, S., and Mowery, K. Putting out a HIT: crowdsourcing malware installs. 5th USENIX conference on offensive technologies, (2011), 9:1--9:10. Google ScholarDigital Library
- Kline, R. B. Principles and Practice of Structural Equation Modeling. Guilford Press, 2011.Google Scholar
- Knijnenburg, B. P., Kobsa, A., and Jin, H. Dimensionality of information disclosure behavior. Int J HumanComputer Studies 71, 12 (2013), 1144--1162. Google ScholarDigital Library
- Knijnenburg, B. P. and Kobsa, A. Making Decisions about Privacy: Information Disclosure in ContextAware Recommender Systems. ACM Transactions on Interactive Intelligent Systems 3, 3 (2013), 20:1--20:23. Google ScholarDigital Library
- Knijnenburg, B. P. and Kobsa, A. Helping users with information disclosure decisions: potential for adaptation. ACM IUI conference (2013), 407--416. Google ScholarDigital Library
- Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., and Newell, C. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22, 4-5 (2012), 441--504. Google ScholarDigital Library
- Knijnenburg, B. P., Willemsen, M. C., and Hirtbach, S. Receiving Recommendations and Providing Feedback: The User-Experience of a Recommender System. In F. Buccafurri and G. Semeraro, eds., E-Commerce and Web Technologies. Springer, 2010, 207--216.Google Scholar
- Kobsa, A., Koenemann, J., Pohl, W. Personalized Hypermedia Presentation Techniques for Improving Customer Relationships. Knowl Eng Rev 16, 2 2001, 111--55. Google ScholarDigital Library
- Kobsa, A. and Teltzrow, M. Contextualized Communication of Privacy Practices and Personalization Benefits: Impacts on Users' Data Sharing Behavior. In D. Martin and A. Serjantov, eds., Privacy Enhancing Technologies: Springer, 2005, 329--343. Google ScholarDigital Library
- Kobsa, A. Privacy-Enhanced Web Personalization. In P. Brusilovsky, A. Kobsa and W. Nejdl, eds., The Adaptive Web: Methods and Strategies of Web Personalization. Springer, 2007, 628--670. Google ScholarDigital Library
- Li, T. and Unger, T. Willing to pay for quality personalization? Trade-off between quality and privacy. Eur J of Information Systems 21, 6 (2012), 621--642.Google ScholarCross Ref
- Lukaszewski, K. M., Stone, D. L., and Stone-Romero, E. F. The Effects of the Ability to Choose the Type of Human Resources System on Perceptions of Invasion of Privacy and System Satisfaction. Journal of Business and Psychology 23, 3-4 (2008), 73--86.Google ScholarCross Ref
- MacKinnon, D. Introduction to Statistical Mediation Analysis. CRC Press, 2007.Google Scholar
- Mayer, R. C., Davis, J. H., and Schoorman, F. D. An Integrative Model Of Organizational Trust. Academy of Management Review 20, 3 (1995), 709--734.Google ScholarCross Ref
- Miyazaki, A. D. and Fernandez, A. Internet Privacy and Security: An Examination of Online Retailer Disclosures. J Public Policy & Marketing 19, 1 (2000), 54--61.Google ScholarCross Ref
- Mossholder, K. W., Giles, W. F., and Wesolowski, M. A. Information privacy and performance appraisal: An examination of employee perceptions and reactions. Journal of Business Ethics 10, 2 (1991), 151--156.Google ScholarCross Ref
- Mulligan, D. and Schwartz, A. Your Place or Mine?: Privacy Concerns and Solutions for Server and Client-Side Storage of Personal Information. Tenth conference on Computers, Freedom and Privacy, (2000), 81--84. Google ScholarDigital Library
- Muslukhov, I., Boshmaf, Y., Kuo, C., Lester, J., and Beznosov, K. Understanding Users' Requirements for Data Protection in Smartphones. ICDEW 2012, 228--35. Google ScholarDigital Library
- Newman, G. H. and Enscoe, C. J. System and method for providing client side personalization of content of web pages and the like. 2000. http://www.google.com/patents?id=VI0EAAAAEBAJ.Google Scholar
- Norberg, P. A., Horne, D. R., Horne, D. A. The Privacy Paradox: Personal Information Disclosure Intentions versus Behaviors. J Consum Aff 41, 1 (2007), 100--126.Google ScholarCross Ref
- Oulasvirta, A. and Sumari, L. Mobile kits and laptop trays: managing multiple devices in mobile information work. CHI 2007, 1127--1136. Google ScholarDigital Library
- Pirim, T., James, T., Boswell, K., Reithel, B., and Barkhi, R. An Empirical Investigation of an Individual's Perceived Need for Privacy and Security. Int'l Journal of Information Security and Privacy 2, 1 (2008), 42--53.Google Scholar
- Ponemon. 2012 Most Trusted Companies for Privacy. Ponemon Institute, 2013. http://www.ponemon.org/local/upload/file/2012%20MTC%20Report%20FINAL.pdfGoogle Scholar
- Schmidt, E. 36h MacTaggart Lecture. 2011. http:// www.youtube.com/watch?v=hSzEFsfc9Ao#t=1224s.Google Scholar
- Shen, X., Tan, B., and Zhai, C. Implicit user modeling for personalized search. ACM ICIKM 2005, 824--831. Google ScholarDigital Library
- Shin, D.-H. The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption. Interacting with Computers 22, 5 (2010), 428--438. Google ScholarDigital Library
- Smith, H. J., Dinev, T., and Xu, H. Information Privacy Research: An Interdisciplinary Review. MIS Quarterly 35, 4 (2011), 989--1016. Google ScholarDigital Library
- Solove, D. J. A Taxonomy of Privacy. University of Pennsylvania Law Review 154, 3 (2006), 477--564.Google ScholarCross Ref
- Spiekermann, S., Grossklags, J., Berendt, B. E-privacy in 2nd Generation E-Commerce: Privacy Preferences versus Actual Behavior. ACM EC 2001, 38--47. Google ScholarDigital Library
- Teltzrow, M. and Kobsa, A. Impacts of User Privacy Preferences on Personalized Systems: a Comparative Study. In C.-M. Karat, J. Blom and J. Karat, eds., Designing Personalized User Experiences for eCommerce. Kluwer, 2004, 315--332. Google ScholarDigital Library
- Toch, E., Wang, Y., and Cranor, L. F. Personalization and Privacy: A Survey of Privacy Risks and Remedies in Personalization-Based Systems. User Modeling and User-Adapted Interaction 22, 1-2 (2012), 203--220. Google ScholarDigital Library
- Toubiana, V., Narayanan, A., Boneh, D., Nissenbaum, H., and Barocas, S. Adnostic: Privacy Preserving Targeted Advertising. NDSS, (2010) http://www.nyu.edu/pages/projects/nissenbaum/papers/adnostic.pdf.Google Scholar
- Xu, H., Luo, X. (Robert), Carroll, J. M., Rosson, M. B. The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decis Support Syst 51, 1 (2011), 42--52. Google ScholarDigital Library
Index Terms
- Let's do it at my place instead?: attitudinal and behavioral study of privacy in client-side personalization
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